用少量标注数据让AI高效诊断胸部X光，效果比零样本提升20%以上。
Label-Efficient Chest X-ray Diagnosis via Partial CLIP Adaptation
- 只微调CLIP视觉编码器部分，实现标签高效诊断。
- 每病种仅1-16个标注样本时，平均AUC提升超20%。
- 适合医疗场景中标注稀缺的疾病筛查，尤其对罕见病有效。
当前医学影像深度学习多依赖大规模标注数据，但实际中因隐私、成本及病例稀缺难以获取。本文提出一种标签高效的胸部X光诊断策略，模拟真实医院场景：图像档案存在但标注稀疏。实验基于NIH Chest X-ray14数据集和预训练的CLIP ViT-B/32模型，通过部分微调其视觉编码器，评估零样本与少样本学习（每病种1–16个标注样本）性能。结果表明，预训练的视觉语言特征可有效适应少样本医学任务，相比零样本基线，平均AUC提升超过20%。该方法为常见与罕见病诊断提供了一种实用且可扩展的解决方案。本研究仅用于学术实验，尚未经过同行评审。代码已开源于https://github.com/heet007-code/CLIP-disease-xray。
原文摘要 · Abstract (English)
Modern deep learning implementations for medical imaging usually rely on large labeled datasets. These datasets are often difficult to obtain due to privacy concerns, high costs, and even scarcity of cases. In this paper, a label-efficient strategy is proposed for chest X-ray diagnosis that seeks to reflect real-world hospital scenarios. The experiments use the NIH Chest X-ray14 dataset and a pre-trained CLIP ViT-B/32 model. The model is adapted via partial fine-tuning of its visual encoder and then evaluated using zero-shot and few-shot learning with 1-16 labeled examples per disease class. The tests demonstrate that CLIP's pre-trained vision-language features can be effectively adapted to few-shot medical imaging tasks, achieving over 20\% improvement in mean AUC score as compared to the zero-shot baseline. The key aspect of this work is to attempt to simulate internal hospital workflows, where image archives exist but annotations are sparse. This work evaluates a practical and scalable solution for both common and rare disease diagnosis. Additionally this research is intended for academic and experimental purposes only and has not been peer reviewed yet. All code is found at https://github.com/heet007-code/CLIP-disease-xray.
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